DZone Cassandra Data Modeling Webinar

Modeling Data In Cassandra
Conceptual Differences Versus RDBMS
Matthew F. Dennis, DataStax // @mdennis

June 27, 2012

Cassandra Is Not Relational
get out of the relational mindset when working
with Cassandra (or really any NoSQL DB)

Work Backwards From Queries
Think in terms of queries, not in terms of
normalizing the data; in fact, you often want to
denormalize (already common in the data
warehousing world, even in RDBMS)

OK great, but how do I do that?
Well, you need to know how Cassandra models
data (e.g. Google Big Table)

research.google.com/archive/bigtable-osdi06.pdf

Go Read It!

In Cassandra:

data is organized into Keyspaces (usually one per app)
➔

each Keyspace can have multiple Column Families
➔

each Column Family can have many Rows
➔

each Row has a Row Key and a variable number of Columns
➔

each Column consists of a Name, Value and Timestamp
➔

In Cassandra, Keyspaces:
are similar in concept to a “database” in some RDBMs
➔

are stored in separate directories on disk
➔

are usually one-one with applications
➔

are usually the administrative unit for things related to ops
➔

contain multiple column families
➔

In Cassandra, In Keyspaces, Column Famlies:
➔ are similar in concept to a “table” in most RDBMs

➔ are stored in separate files on disk (many per CF)

➔ are usually approximately one-one with query type

➔ are usually the administrative unit for things related to your data

➔ can contain many (~billion* per node) rows

* for a good sized node
(you can always add nodes)

In Cassandra, In Keyspaces, In Column Families ...

Rows

thepaul office: Austin OS: OSX twitter: thepaul0

mdennis office: UA OS: Linux twitter: mdennis

thobbs office: Austin twitter: tylhobbs

Row Keys




Columns

Column Names




Column Values







Rows Are Randomly Ordered
(if using the RandomPartitioner)




Columns Are Ordered by Name
(by a configurable comparator)

Columns are ordered because
doing so allows very efficient
implementations of useful and
common operations

(e.g. merge join)

In particular, within a row
columns with a given name can
be located very quickly.
(ordered names => log(n) binary search)

More importantly, I can query for a
slice between a start and end

Row Key

RK ts0 ts1 ... ... tsM ... ... ... ... tsN ... ... ... ... ...

start end

Why does that matter?
Because columns within don’t have to be static!
(and random disk seeks are teh evil)

The Column Name Can Be Part of Your Data

INTC ts0: $25.20 ts1: $25.25 ...

AMR ts0: $6.20 ts9: $0.26 ...

CRDS ts0: $1.05 ts5: $6.82 ...

Columns Are Ordered by Name
(in this case by a TimeUUID Comparator)

Turns Out That Pattern Comes Up A Lot
➔ stock ticks
➔ event logs

➔ ad clicks/views

➔ sensor records

➔ access/error logs

➔ plane/truck/person/”entity” locations

➔…

OK, but I can do that in SQL
Not efficiently at scale, at least not easily ...

How it Looks In a RDBMS
ticker timestamp bid ask ...
AMR ts0 ... ... ...
... ... ... ... ...
CRDS ts0 ... ... ...
... ... ... ... ...
Data I Care About ... ts0 ... ... ...
AMR ts1 ... ... ...
... ... ... ... ...
... ... ... ... ...
… ts1 ... ... ...
AMR ts2 ... ... ...
... ts2 ... ... ...

How it Looks In a RDBMS
ticker timestamp bid ask ...
AMR ts0 ... ... ...

Larger Than Your Page Size
Disk Seeks
AMR ts1 ... ... ...

Larger Than Your Page Size

AMR ts2 ... ... ...
... ts2 ... ... ...

OK, but what about ...
PostgreSQL Cluster Command?
➔

MySQL Cluster Indexes?
➔

Oracle Index Organized Tables?
➔

SQLServer Clustered Index?
➔

OK, but what about ...
PostgreSQL Cluster Using?
➔

Meh ...
MySQL [InnoDB] Cluster Indexes?
➔

Oracle Index Organized Table?
➔

SQLServer Clustered Index?
➔

The on-disk management of that
clustering results in tons of IO …

In the case of PostgreSQL:

clustering is a one time operation
➔

(implies you must periodically rewrite the entire table)

new data is *not* written in clustered order
➔

(which is often the data you care most about)

OK, so just partition the tables ...

Not a bad idea, except in MySQL there is a limit of
1024 partitions and generally less if using NDB

(you should probably still do it if using MySQL though)

http://dev.mysql.com/doc/refman/5.5/en/partitioning-limitations.html

OK fine, I agree storing data that is queried
together on disk together is a good thing but
what's that have to do with modeling in
Cassandra?

Seek To Here

RK ts0 ts1 ... ... tsM ... ... ... ... tsN ... ... ... ... ...

Read Precisely My Data *

* more on some caveats later

Well, that's what is meant by “work backwards
from your queries” or “think in terms of queries”

(NB: this concept, in general, applies to RDBMS
at scale as well; it is not specific to Cassandra)

An Example From Fraud Detection
To calculate risk it is common to need to know all the
emails, destinations, origins, devices, locations, phone
numbers, et cetera ever used for the account in question

In a normalized model that usually translates to a
table for each type of entity being tracked

id name ... id device ...
1 guy ... 1000 0xdead ...
2 gal ... 2000 0xb33f ...
... ... ... ... ... ...

id dest ... id email ... id origin ...
15 USA ... 100 guy@ ... 150 USA ...
25 Finland ... 200 gal@ ... 250 Nigeria ...
... ... ... ... ... ... ... ... ...

The problem is that at scale that also means
a disk seek for each one …
(even for perfect IOT et al if across multiple tables)

➔Previous emails? That's a seek …
➔Previous devices? That's a seek …

➔Previous destinations? That's a seek ...

But In Cassandra I Store The Data I Query
Together On Disk Together
(remember, column names need not be static)

Data I Care About

acctY ... ... ... ... ... ... ...
acctX dest21 dev2 dev7 email3 email9 orig4 ...
acctZ ... ... ... ... ... ... ...

email:cassandra@mailinator.com = dateEmailWasLastUsed

Column Name Column Value

Don't treat Cassandra (or any DB) as a black box
➔Understand how your DBs (and data structures) work

➔Understand the building blocks they provide

➔Understand the work complexity (“big O”) of queries

➔For data sets > memory, goal is to minimize seeks *

* on a related note, SSDs are awesome

Q?
Modeling Data In Cassandra
Conceptual Differences Versus RDBMS
Matthew F. Dennis, DataStax // @mdennis

DZone Cassandra Data Modeling Webinar

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